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An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19
Journal
Computational and Mathematical Methods in Medicine
Date Issued
2021
Author(s)
Woan Ching Serena Low
Joon Huang Chuah
Clarence Augustine T. H. Tee
Shazia Anis
Muhammad Ali Shoaib
Amir Faisal
Azira Binti Khalil
Khin Wee Lai
DOI
10.1155/2021/5528144
Abstract
Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant’s technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.
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